Deardon, RobJafari, Behnaz2020-01-312020-01-312020-01-30Jafari, B. (2020). Bias and Bias-Correction for Individual-Level Models of Infectious Disease (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.http://hdl.handle.net/1880/111598Accurate infectious disease models can help scientists understand how an ongoing disease epidemic spreads and help forecast the course of epidemics more effectively (e.g. O'Neill, 2010; Jewell et al., 2009; Deardon et al., 2010). The main purpose of infectious disease modeling is to capture the main risk factors that affect the spread of a disease and make a prediction based on these factors. In real life, we do not generally have homogeneous and homogeneously mixing populations and various factors affect the spread of a disease (e.g. geographical, social, domestic, and employment networks, genetics factors). Using individual-level-models (ILMs) (Deardon et al., 2010) can help researchers to incorporate population heterogeneity. In these models inferences are made within a Bayesian Markov chain Monte Carlo (MCMC) framework (e.g. Gamerman and Lopes, 2006), obtaining posterior estimates of model parameters. However, parameter estimation and bias of estimates go hand in hand. The issue of bias of parameter estimates, and methods for bias correction, have been widely studied in the context of many of the most established and commonly used statistical models, and associated methods of parameter estimation. However, these methods are not directly applicable to individual-level infections disease data. The focus of this thesis is to investigate circumstances in which ILM parameter estimates may be biased in some simple disease system scenarios. Further, we aim to find bias-corrected estimates of ILM parameters using simulation and compare them with the posterior estimates of the model parameter. We also discuss the factors that affect performance of these estimators.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.infectious disease model, disease epidemic, individual-level-models (ILMs), Markov chain Monte Carlo (MCMC), parameter estimation, posterior estimate, bias, bias correctionStatisticsmaster thesis10.11575/PRISM/37542